Document Type : Review Paper
Authors
1 Computer Science Department, Al-Ma’arif University College, Ramadi, Iraq
2 College of Sciences Al-Nahrain University, Baghdad, Iraq
3 Head of Quality Assurance and Accridition, University of Fallujah, Fallujah, Iraq
Abstract
Cloud computing is an evolving and high-demand research field at the
forefront of technological advancements. It aims to provide software resources and
operates based on service-oriented delivery. Within the infrastructure as a service (IaaS)
framework, the cloud offers end customers access to crucial infrastructure resources,
including CPU, bandwidth, and memory. When a cloud system fails to deliver as
expected, it is referred to as an event, signifying a deviation from the anticipated service.
To meet their service-level agreement (SLA) obligations, cloud service providers (CSPs)
must ensure continuous access to fault-tolerant, on-demand resources for their clients,
particularly during outages. Consequently, finding the most efficient ways to accomplish
tasks while considering the rapid depletion of resources has become an urgent concern.
Researchers are actively working to develop optimal strategies tailored to the cloud
environment. Machine learning plays a critical role in these endeavors, serving as a key
component in various cloud computing platforms. This study presents a comprehensive
literature review of current research papers that employ machine learning algorithms to
propose strategies for optimizing cloud computing environments. Additionally, the survey
provides authors with invaluable resources by extensively exploring a diverse range of
machine learning techniques and their applications in the field of cloud computing. By
examining these areas, researchers aim to enhance their understanding of efficient
resource allocation and scheduling, addressing the challenges posed by resource scarcity
while meeting SLA obligations.
Keywords
- Cloud computing
- Resource allocation Prediction
- Energy-efficient resource management
- Task Scheduling
- Machine Learning
Main Subjects
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